TY - GEN
T1 - Homomorphic Encryption Based Secure Sensor Data Processing
AU - Gadepally, Vijay
AU - Isakov, Mihailo
AU - Agrawal, Rashmi
AU - Kepner, Jeremy
AU - Gettings, Karen
AU - Kinsy, Michel A.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/22
Y1 - 2020/9/22
N2 - Novel sensor processing algorithms face many hurdles to their adoption. Sensor processing environments have become increasingly difficult with an ever increasing array of threats. These threats have, in turn, raised the bar on deploying new capabilities. Many novel sensor processing algorithms exploit or induce randomness to boost algorithm performance. Co-designing this randomness with cryptographic features could be a powerful combination providing both improved algorithm performance and increased resiliency. The emerging field of signal processing in the encrypted domain has begun to explore such approaches. The development of this new class of algorithms will require new classes of tools. In particular, the foundational linear algebraic mathematics will need to be enhanced with cryptographic concepts to allow researchers to explore this new domain. This work highlights a relatively low overhead method that uses homomorphic encryption to enhance the resiliency of a part of a larger sensor processing pipeline.
AB - Novel sensor processing algorithms face many hurdles to their adoption. Sensor processing environments have become increasingly difficult with an ever increasing array of threats. These threats have, in turn, raised the bar on deploying new capabilities. Many novel sensor processing algorithms exploit or induce randomness to boost algorithm performance. Co-designing this randomness with cryptographic features could be a powerful combination providing both improved algorithm performance and increased resiliency. The emerging field of signal processing in the encrypted domain has begun to explore such approaches. The development of this new class of algorithms will require new classes of tools. In particular, the foundational linear algebraic mathematics will need to be enhanced with cryptographic concepts to allow researchers to explore this new domain. This work highlights a relatively low overhead method that uses homomorphic encryption to enhance the resiliency of a part of a larger sensor processing pipeline.
KW - homomorphicencryption
KW - matrix operations
KW - multiparty secure computing
KW - sensor data
UR - http://www.scopus.com/inward/record.url?scp=85099345881&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099345881&partnerID=8YFLogxK
U2 - 10.1109/HPEC43674.2020.9286175
DO - 10.1109/HPEC43674.2020.9286175
M3 - Conference contribution
AN - SCOPUS:85099345881
T3 - 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
BT - 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
Y2 - 21 September 2020 through 25 September 2020
ER -